GraphOracle: Efficient Fully-Inductive Knowledge Graph Reasoning via Relation-Dependency Graphs

Tue, 25 Mar 2025 00:00:00 +0000

Abstract

Knowledge-graph reasoners often struggle when test-time entities and relations were never observed during training. GraphOracle addresses this fully-inductive setting by converting each knowledge graph into a directed Relation-Dependency Graph (RDG). The RDG captures how relations compose, and its query-aware embeddings guide answer prediction on the original entity graph.

Motivation

Relation order matters: “born in → located in” carries a compositional direction that an undirected relation graph erases.

Existing fully-inductive methods construct dense, undirected relation graphs. This makes computation expensive and loses directional dependencies that are essential for multi-hop reasoning. The same limitation makes it difficult to transfer a learned reasoner across domains with different entity and relation vocabularies.

Method

GraphOracle operates in three stages:

RDG Construction — Transform the KG into a directed Relation-Dependency Graph where edge (r_i, r_j) indicates that relation r_i precedes r_j in observed triple chains. This is significantly sparser than prior relation graphs while capturing compositional patterns.
Query-Dependent Multi-Head Attention — For each query relation, a multi-head attention GNN propagates messages over the RDG to produce context-aware relation embeddings. The same relation gets different representations depending on the query context.
Entity-Level Answer Prediction — The learned relation embeddings parameterize a second GNN on the original KG entity graph, performing inductive message passing from the query entity to score candidates.

Experimental Results

Evaluated across 60 benchmarks spanning transductive, entity-inductive, fully-inductive, and cross-domain settings.

MRR Comparison on 60 Datasets

GraphOracle (red) consistently matches or outperforms supervised SOTA (green) across all 60 datasets, with particularly strong gains on cross-domain and biomedical KGs.

Average Performance (4 Settings)

GraphOracle achieves +7.19% MRR improvement in transductive, +10.86% in entity-inductive, +13.28% in fully-inductive, and +26.82% in cross-domain settings over supervised SOTA.

Ablation Study

Removing the RDG structure or multi-head attention causes significant performance drops, confirming both components are essential. The directed precedence encoding is the single most important design choice.

MoKGR: Mixture of Length and Pruning Experts for Knowledge Graphs Reasoning

Sun, 19 Jan 2025 00:00:00 +0000

Abstract

Knowledge-graph reasoning depends on finding the right paths—not simply exploring more of them. MoKGR personalizes graph traversal with two mixture-of-experts modules: one chooses an appropriate reasoning depth for each query, while the other decides which candidate paths deserve to survive.

Motivation

Different questions over the same graph may need completely different search budgets.

The query (JACK, followed, ?) may resolve within three hops, while (JACK, watched, ?) can require deeper exploration. Fixed-depth GNNs spend too much work on simple questions and stop too early on difficult ones. Uniform pruning compounds the problem by applying one notion of relevance to every relation and query.

Method

MoKGR introduces two complementary Mixture-of-Experts modules:

Mixture of Length Experts — Experts specialize at different reasoning depths. A learned gate assigns query-specific weights, routing simple queries toward short paths and complex queries toward deeper ones.
Mixture of Pruning Experts — At each layer, multiple experts judge candidate paths from complementary perspectives before a learned aggregation keeps the most informative ones.
End-to-End Training — Both modules are trained jointly with the answer prediction objective, ensuring optimal synergy between depth selection and path pruning.

Experimental Results

Transductive Setting (6 Benchmarks)

MoKGR achieves the best results across all 6 benchmarks (Family, UMLS, WN18RR, FB15k237, NELL-995, YAGO3-10), outperforming both non-GNN baselines and state-of-the-art GNN methods including NBFNet, RED-GNN, A*Net, and AdaProp.

Efficiency & Convergence

MoKGR converges significantly faster than competing methods while maintaining lower inference time, demonstrating that personalized path exploration is both more accurate and more efficient.

Expert Selection Analysis

The learned gating weights show interpretable patterns: the model prefers medium-length paths overall, but adapts dynamically — shorter paths for simple relational queries, deeper paths for complex multi-hop reasoning.

Ablation Study

Removing the length experts causes the largest drop, confirming that adaptive depth is the most critical innovation. Removing pruning experts or using a single expert also degrades performance.

Knowledge Graphs | Enjun Du（杜恩俊）

GraphOracle: Efficient Fully-Inductive Knowledge Graph Reasoning via Relation-Dependency Graphs

Abstract

Motivation

Method

Experimental Results

MRR Comparison on 60 Datasets

Average Performance (4 Settings)

Ablation Study

MoKGR: Mixture of Length and Pruning Experts for Knowledge Graphs Reasoning

Abstract

Motivation

Method

Experimental Results

Transductive Setting (6 Benchmarks)

Efficiency & Convergence

Expert Selection Analysis

Ablation Study